Skip to main content

Evals consulting for AI agents

Clarity by Epistemic Me runs evals consulting for AI agents, done-with-you or done-for-you, as part of its AI-native services. We help your team measure whether an agent matches expert judgment, find where it fails, and improve it with evidence.

Engagements start at $100k+. Official partner of Parlance Labs.

Scope your agent engagement

Who it is for

Teams shipping an AI agent that must match expert judgment

Your agent does work that a domain expert reviews today. The demo works, but nobody can say how often the agent is right, which failure modes matter, or whether the last prompt change made things better or worse.

  • You need AI agent evaluation that your experts agree with, not a generic score.
  • You fix one case and another one breaks.
  • You need evidence before you roll the agent out further.

Without an eval loop

  • ×Change the prompt, try a few cases, and hope the agent is better
  • ×Failures are single cases with no shared cause
  • ×No record of what the experts would accept

With an eval loop

  • ✓Each change is scored on a golden dataset labeled by experts
  • ✓Failures are grouped into named failure modes
  • ✓A regression gate in CI blocks a change that breaks a known case

Two ways to work

Done-with-you or done-for-you

Done-with-you

We run the eval loop with your engineers and domain experts, and teach them to own it.

What you get

  • Error analysis on your production traces, done together
  • A failure taxonomy and an expert rubric your team helped write
  • A team that can run the loop after the engagement ends

Done-for-you

We build and run the evals, and the agent improvements, for you.

What you get

  • A golden dataset labeled against your experts' rubric
  • An LLM-as-judge checked against expert labels
  • Regression gates in CI, plus agent changes measured against the baseline

The method

From production traces to regression gates

The same loop runs in both modes. The difference is who does the work.

  1. Look at production traces

    Read what the agent actually did on real inputs before deciding what to measure.

  2. Error analysis

    Group the failures into a failure taxonomy, so you fix failure modes instead of single cases.

  3. Expert rubric

    Write pass and fail criteria with the domain experts who judge the work today.

  4. Golden dataset

    Label representative cases against the rubric. This becomes the reference for every later change.

  5. LLM-as-judge, checked against expert labels

    Measure judge agreement with expert labels: how often the judge gives the same label as your experts. Trust the judge only where it agrees.

  6. Regression gates

    Run the evals as regression tests in CI, so a change that breaks a known case does not ship.

Deliverables

You own what we build

Your team owns the dataset, rubric, judge and code. The IP stays with you, and the evals keep running after the engagement ends.

  • Failure taxonomy from error analysis
  • Expert rubric
  • Golden dataset
  • LLM-as-judge and its agreement with expert labels
  • Regression tests that run in CI
  • The code for all of the above

Proof · Dayforce

Human expert alignment at Dayforce

Nearly

60%

improvement in human expert alignment scores

Lynn Chyi, AI Experience Architect at Dayforce, says Clarity helped by "improving our human expert alignment scores by nearly 60%." The score measures how close the agents' judgment came to a human expert's.

Read the Dayforce case study

Proof

Teams at Dayforce, Delphi and Blueprint on working with Clarity

“Clarity helped us achieve that by improving our human expert alignment scores by nearly 60%.”

Lynn Chyi, AI Experience Architect, Dayforce
Read the full transcript

Hi, my name is Lynn, and I'm an AI experience architect here at Dayforce. Our team brought in Clarity to provide much needed expertise for building and improving our AI agents. As you know, standing up agents, well, that's the easy part. And getting them beyond a demo and ready for the scale that we needed was not that straightforward. So specifically, we brought them in to tell us how agents are measured and evaluated and how to improve them. And as anyone who has ever built agents before, you know, this is not that trivial. The agents that we were building had to do really complex work. Now, these are agents that need to read documents and extract specialized domain knowledge in a way that makes sense to the business, both with high recall and high precision. And we didn't want to compromise on the accuracy we'd get from a human, but we wanted to reduce the number of hours it took for them to do it by about half or more. So this meant that we needed agents to operate with judgment nearly indistinguishable from a human expert. And Clarity helped us achieve that by improving our human expert alignment scores by nearly 60%. So we went from building agents just by guessing its system prompts and skills and tools with somewhat inconsistent results to deploying reliable agents that delivered real business value. So, look, I can't understate how valuable it is to have someone like Clarity and bring them into your team to provide the industry's best practices on how to ship agents that you can really measure and trust.

  • “introduced a couple frameworks that we had never heard of that have very clear steps that we can take this week”

    Sam Spelsberg, CTO & Co-Founder, Delphi AI

  • “see how we can enumerate a failure taxonomy which they introduced us to”

    Alvin Alaphat, Founding Engineer, Delphi AI

  • “We suddenly had a clear way to see what was working, what wasn't, and how to fix it.”

    Greg Fong, Founding Engineer, Blueprint by Bryan Johnson

Official partner of Parlance Labs

Clarity is an official partner of Parlance Labs, Hamel Husain's evals training company, which teaches the course "AI Evals For Engineers & PMs".

Engagements start at $100k+

Evals consulting is part of Clarity's AI-native services. Scope, deliverables and duration are agreed in the proposal. Your traces and expert labels are client data; read how we handle data and access on our security page.

Questions buyers ask

Who offers evals consulting for AI agents?

Clarity by Epistemic Me offers evals consulting for AI agents as part of its AI-native services, done-with-you or done-for-you. Clarity is an official partner of Parlance Labs.

What does an evals engagement deliver?

A failure taxonomy from error analysis on your traces, a rubric written with your domain experts, a golden dataset, an LLM-as-judge checked against expert labels, and regression gates in CI. Your team owns the dataset, rubric, judge and code.

Do we need an eval tool or an eval service?

An eval tool gives you a place to run and store evals. It does not decide what to measure or whether your judge agrees with your experts. An eval service does that work with your team. Compare an eval tool and an eval service.

How do you know an LLM judge can be trusted?

Check it against expert labels. Your domain experts label a set of cases, the judge labels the same cases, and you measure how often they agree. Use the judge only where agreement is acceptable, and check it again when the agent or the rubric changes.

What does an evals engagement cost?

Evals consulting is part of Clarity's AI-native services, which start at $100k+. Scope, deliverables and duration are agreed in the proposal.

Bring one agent and the experts who judge it

Describe the workflow in the services qualification form. The scoping conversation sets fit, mode and the first milestone.

Scope your agent engagement